Palo Alto CEO Nikesh Arora demands 90% drop in AI token costs for mass enterprise adoption

Palo Alto CEO Arora says AI pricing needs to fall 90% as token costs skyrocket

Palo Alto Networks CEO: We need to see the pricing for AI come down Palo Alto Networks CEO Nikesh Arora warned that token costs need to drop as much as 90% to promote large-scale artificial intelligence adoption. "I think 54% is a good start," Arora told CNBC's Seema Mody on "Squawk on the Street" Thursday, after OpenAI CEO Sam Altman told CNBC that the frontier lab's latest model is 54% more token-efficient for agentic coding. "I think we probably need another turn at it." Arora said token efficiency needs to drop to as much as 20% over the next twelve months, and 90% by the following year. Rising token costs have emerged as a major pain point for businesses and put a strain on AI budgets. The current pricing, he said, makes AI tools increasingly difficult for businesses to implement. "We need to see the pricing for AI come down," Arora said. Arora is among a growing group of executives pushing for a decline in token pricing. The worry is that high token costs create a major barrier to widespread adoption, preventing many enterprises from using the tools.

Palo Alto CEO: AI token prices must fall up to 90%

Palo Alto Networks CEO Nikesh Arora told CNBC that AI token prices need to fall by as much as 90% for large-scale enterprise adoption, calling OpenAI's 54% GPT-5.6 efficiency gain "a good start" but not enough. He argued demand is "infinite" and costs will "rationalize over time." His plea reflects a real paradox: per-token prices have collapsed while total enterprise AI bills keep rising, driven by agentic usage. Palo Alto Networks chief executive Nikesh Arora says the cost of running AI needs to plunge before businesses can deploy it at scale. He told CNBC on Thursday that token prices may need to fall by as much as 90%, according to CNBC. Arora was reacting to OpenAI's claim that its new GPT-5.6 model is 54% more token-efficient on agentic coding. "I think 54% is a good start," he said, making clear it is nowhere near enough. He wants the trend to continue, with efficiency improving further over the next year and dramatically more the year after. Only then, in his telling, does mass enterprise adoption become affordable. Despite the sticker shock, Arora is not bearish on demand. "The demand continues to be infinite," he said, arguing that with an infinite demand curve, costs "will rationalize over time". His logic is that the market will either grow into the spending or force prices down. Budgets should ease, he suggested, as the underlying technology becomes more efficient. The paradox behind the plea Arora's complaint captures a genuine puzzle in enterprise AI. Per-token prices have collapsed, yet total bills keep climbing, so much so that prices fell 98% while enterprise AI bills tripled. The culprit is agentic AI, which calls a model over and over to complete a task. A single ambitious project can burn through a fortune, as one developer's agents ran up a $1.3m token bill in a month. That is why cheaper headline prices do not automatically translate into lower costs. Usage grows faster than prices fall, and the bill goes up anyway. Squeezed buyers and a price war The strain is already changing behaviour, with some firms capping how much AI staff can use as costs bite. Arora is voicing, from the buyer's seat, a frustration many enterprises share. The good news for him is that a price war is under way, with DeepSeek making a 75% discount permanent and rivals racing to match. A wave of startups is also chasing cheaper inference to squeeze more output from every chip. Whether that adds up to Arora's 90% is another matter, since efficiency gains can be swallowed by ever-heavier usage. His bet is that scale eventually wins, and the economics settle. For now, the man running a cybersecurity giant is effectively telling AI vendors their product is still too expensive to use everywhere he wants to use it. Coming from a customer of that size, it is a message the model makers will hear.

Palo Alto Networks CEO Nikesh Arora: AI token costs must fall 90%

Token prices will need to come down by as much as 90% before large-scale enterprise AI deployment becomes viable, Palo Alto Networks $PANW CEO Nikesh Arora said Thursday, describing today's pricing environment as a practical obstacle for companies trying to roll out the technology. His timeline for relief was specific: within a year, costs should shrink to roughly a fifth of where they stand today, and by the year after that, to just a tenth. The remarks came after OpenAI CEO Sam Altman announced that the company's newest model delivers 54% better token efficiency on agentic coding tasks -- a figure Arora welcomed but treated as a floor rather than a ceiling. "I think 54% is a good start," he said. "I think we probably need another turn at it." Even so, Arora stopped short of pessimism about where things are headed. "The demand continues to be infinite, and as long as you have an infinite demand curve that you're facing, I think all these things will rationalize over time," Arora said. He suggested that growing efficiency in the underlying models would eventually take pressure off corporate AI spending. His concerns put him in company with a widening circle of corporate leaders who have grown vocal about what they see as prohibitive model pricing -- costs high enough, in their view, to keep AI from moving beyond pilots into genuine enterprise-wide use. Palantir $PLTR Technologies CEO Alex Karp made similar noise last week, going after the per-token approach that both Anthropic and OpenAI rely on and pointing to open-weight models as a more workable path for enterprise customers. "Something has gone completely wrong," he told CNBC, while noting he was not singling out any particular vendor. The numbers behind the frustration are striking and reflect a broader paradox in enterprise AI: The Next Web reports that headline per-token rates have dropped 98%, yet total enterprise AI spending has tripled over the same period, because agentic applications chain together model calls in ways that multiply consumption far faster than unit prices fall. The strain is already changing corporate behavior. Companies including Uber $UBER and Microsoft $MSFT have capped or restricted employee access to expensive AI coding tools after budgets blew past projections. Some firms have moved toward cheaper open-weight models, including Chinese alternatives that are closing the gap with American labs, according to CNBC. None of this friction has slowed the broader buildout. SpaceX tapped debt markets for $25 billion last month, and Amazon $AMZN followed this week with a $25 billion bond raise of its own, with both moves tied to the surging capital demands of AI infrastructure, according to CNBC.

Palo Alto Networks CEO Says Token Costs Slow Enterprise AI Adoption | PYMNTS.com

Speaking on CNBC's "Squawk on the Street," Arora said the cost needs to drop 20% over the next 12 months and 90% by the following year, CNBC reported Thursday. Asked about OpenAI CEO Sam Altman's comments to CNBC that OpenAI's latest model is 54% more efficient for coding, Arora said, "I think 54% is a good start. I think we probably need another turn at it." It was reported in June that after seeing the costs of AI rise, companies are looking to better manage their use of the technology. Companies that encouraged their employees to use AI tools when the costs were lower are now using a variety of methods to cut back, such as introducing usage caps, encouraging employees to use the right tool for each task, sharing cost-saving ideas such as switching to models that are older and cheaper, and adopting open-source models. The report also found that there is an opening for Chinese AI labs that are able to charge less than the U.S. companies due to their more efficient models and China's lower energy costs. It was reported in May that "token shock" had hit some of Silicon Valley's biggest spenders. For example, Uber exhausted its full-year 2026 AI budget by April, leading Chief Technology Officer Praveen Neppalli Naga to say the company was "back to the drawing board" and Chief Operating Officer Andrew Macdonald to say Uber would weigh token costs directly against the cost of hiring engineers. PYMNTS reported at the time that agentic coding tools compound the cost exposure relative to standard chatbot interactions because while a single-turn conversation generates one inference call, an agentic session generates many more. The PYMNTS Intelligence report "The Enterprise AI Benchmark Report: Financial Services Pulls Ahead in the Enterprise AI Race" found that companies across financial services and insurance, healthcare, and media and advertising are putting more money behind AI. As they do so, these enterprises are beginning to decide which projects deserve real capital and which still need proof, the report said.